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HASK | COLLOCATION DATABASES
Citation Please cite this paper in publications using HASK for the BNC: Pezik, Piotr. Graph-Based Analysis of Collocational Profiles. In Phraseologie Im Wörterbuch Und Korpus (Phraseology in Dictionaries and Corpora), edited by Vida Jesenšek and Peter Grzybek, 227–43. ZORA 97. Maribor, Bielsko‑Biała, Budapest, Kansas, Praha: Filozofska fakuteta, 2014.
150,045entries containing2,875,407combinations...
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# Collocate POS A TTEST MI3 CHISQ JD R AWT fAWT Details Equivalents
1. have 've
had
has
have
having 		Verb 21 -24.54 6.12 105.66 0.72 14 7,238,291 7.51
2. be 's
are
be
been
being
is
was
were 		Verb 242 -12.13 15.01 123.92 0.72 44 1,964,328 27.67
3. do did
do
does 		Verb 16 -9.99 6.19 29.82 0.62 9 13,366,024 4.07
4. say said
say
says 		Verb 7 -9.91 3.37 21.24 0.58 6 12,592,322 4.32
5. would would Verb 8 -8.01 4.06 17.15 0.57 6 16,853,130 3.23
6. will will Verb 8 -6.79 4.23 13.86 0.51 5 16,513,043 3.29
7. take taken
takes 		Verb 5 -5.9 2.78 9.71 0.48 4 22,629,155 2.4
8. can can Verb 14 -3.6 6.64 6.75 0.69 11 10,961,384 4.96
9. make made
make
makes
making 		Verb 11 -3.31 5.92 5.58 0.68 10 8,417,662 6.46
10. use use
used
using 		Verb 6 -2.73 4.09 3.56 0.53 5 12,865,959 4.23
11. give gave
give
given
giving 		Verb 13 -0.03 7.39 0 0.7 11 8,318,420 6.53
12. understand understand
understanding 		Verb 5 1.19 5.74 3.01 0.34 3 27,073,151 2.01
13. may may Verb 17 1.26 8.7 2.29 0.72 13 6,739,386 8.07
14. might might Verb 12 1.67 8.12 5.45 0.58 7 15,557,890 3.49
15. refer refer
referred
refers 		Verb 6 1.87 7.24 14.61 0.53 5 16,392,169 3.32
16. 's 's Verb 10 1.87 7.93 8.53 0.35 4 24,398,835 2.23
17. match Match
matched 		Verb 5 2 7.87 37.36 0.17 2 29,792,690 1.82
18. interpret interpret
interpreted
interpreting 		Verb 7 2.48 9.6 97.55 0.58 6 24,193,263 2.25
19. contain contain
contained
containing
contains 		Verb 10 2.57 9.06 35.37 0.54 7 18,041,379 3.01
20. parse parse
parsed 		Verb 15 3.87 18.62 26,798.46 -0.01 1 54,356,094 1
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